From Raw Data to Operational Insight: A Machine Learning Approach for La Logistica S.r.l.

This paper illustrates an original Data-Driven Analysis on logistics matters, developed within the scope of a research & development project conducted by La Logistica S.r.l. and financed by the Apulia Region. The paper uses real operative data for developing and validating a complete analytical tool to forecast volume saturation on pallets. Compared to other studies on similar matters, which frequently make use of simulated and ideal sets of data, this research provides a unique opportunity to test, on an empirical ground, how very advanced Data Science approaches could be successfully applied on industrial processes.The paper develops a complete analytical investigation combining diagnose analytics, forecasting and model-interpretability to compare the performances of many different Machine Learning algorithms, such us K-Nearest Neighbors, Decision Trees, Random Forest, Boosting, Support Vector Machines, and Linear Models. The results obtained after an overarching comparison show how KNN is significantly more accurate and trustful in relation to any other method, outperforming any other on any key error measure and interpretability factors. The addition of contribution and importance investigation on variables extends to this paper a unique degree of originality, showing which SKUs’ physical variables are most significantly influencing volume saturation on pallets, and offering immediate management implications related to their operative meaning. The complete methodological pipeline is grounded on real operative data regarding La Logistica S.r.l., making this paper capable to show how operative Data-Driven solutions could play an innovative role into improving operative continuity within logistics matters.